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Africa
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America
AdakAnchorageAnguillaAntiguaAraguaina
Argentina
Buenos_AiresCatamarcaComodRivadaviaCordobaJujuyLa_RiojaMendozaRio_GallegosSaltaSan_JuanSan_LuisTucumanUshuaia
ArubaAsuncionAtikokanAtkaBahiaBahia_BanderasBarbadosBelemBelizeBlanc-SablonBoa_VistaBogotaBoiseBuenos_AiresCambridge_BayCampo_GrandeCancunCaracasCatamarcaCayenneCaymanChicagoChihuahuaCiudad_JuarezCoral_HarbourCordobaCosta_RicaCrestonCuiabaCuracaoDanmarkshavnDawsonDawson_CreekDenverDetroitDominicaEdmontonEirunepeEl_SalvadorEnsenadaFort_NelsonFort_WayneFortalezaGlace_BayGodthabGoose_BayGrand_TurkGrenadaGuadeloupeGuatemalaGuayaquilGuyanaHalifaxHavanaHermosilloIndiana
IndianapolisInuvikIqaluitJamaicaJujuyJuneauKentucky
Knox_INKralendijkLa_PazLimaLos_AngelesLouisvilleLower_PrincesMaceioManaguaManausMarigotMartiniqueMatamorosMazatlanMendozaMenomineeMeridaMetlakatlaMexico_CityMiquelonMonctonMonterreyMontevideoMontrealMontserratNassauNew_YorkNipigonNomeNoronhaNorth_Dakota
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Arctic
Asia
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@ -0,0 +1,28 @@
|
||||
import pytest
|
||||
|
||||
from pandas import (
|
||||
Categorical,
|
||||
CategoricalDtype,
|
||||
Index,
|
||||
IntervalIndex,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
|
||||
|
||||
class TestAstype:
|
||||
@pytest.mark.parametrize("ordered", [True, False])
|
||||
def test_astype_categorical_retains_ordered(self, ordered):
|
||||
index = IntervalIndex.from_breaks(range(5))
|
||||
arr = index._data
|
||||
|
||||
dtype = CategoricalDtype(None, ordered=ordered)
|
||||
|
||||
expected = Categorical(list(arr), ordered=ordered)
|
||||
result = arr.astype(dtype)
|
||||
assert result.ordered is ordered
|
||||
tm.assert_categorical_equal(result, expected)
|
||||
|
||||
# test IntervalIndex.astype while we're at it.
|
||||
result = index.astype(dtype)
|
||||
expected = Index(expected)
|
||||
tm.assert_index_equal(result, expected)
|
@ -0,0 +1,13 @@
|
||||
from pandas.core.arrays import IntervalArray
|
||||
|
||||
|
||||
def test_repr():
|
||||
# GH#25022
|
||||
arr = IntervalArray.from_tuples([(0, 1), (1, 2)])
|
||||
result = repr(arr)
|
||||
expected = (
|
||||
"<IntervalArray>\n"
|
||||
"[(0, 1], (1, 2]]\n"
|
||||
"Length: 2, dtype: interval[int64, right]"
|
||||
)
|
||||
assert result == expected
|
@ -0,0 +1,231 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import pandas as pd
|
||||
from pandas import (
|
||||
Index,
|
||||
Interval,
|
||||
IntervalIndex,
|
||||
Timedelta,
|
||||
Timestamp,
|
||||
date_range,
|
||||
timedelta_range,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
from pandas.core.arrays import IntervalArray
|
||||
|
||||
|
||||
@pytest.fixture(
|
||||
params=[
|
||||
(Index([0, 2, 4]), Index([1, 3, 5])),
|
||||
(Index([0.0, 1.0, 2.0]), Index([1.0, 2.0, 3.0])),
|
||||
(timedelta_range("0 days", periods=3), timedelta_range("1 day", periods=3)),
|
||||
(date_range("20170101", periods=3), date_range("20170102", periods=3)),
|
||||
(
|
||||
date_range("20170101", periods=3, tz="US/Eastern"),
|
||||
date_range("20170102", periods=3, tz="US/Eastern"),
|
||||
),
|
||||
],
|
||||
ids=lambda x: str(x[0].dtype),
|
||||
)
|
||||
def left_right_dtypes(request):
|
||||
"""
|
||||
Fixture for building an IntervalArray from various dtypes
|
||||
"""
|
||||
return request.param
|
||||
|
||||
|
||||
class TestAttributes:
|
||||
@pytest.mark.parametrize(
|
||||
"left, right",
|
||||
[
|
||||
(0, 1),
|
||||
(Timedelta("0 days"), Timedelta("1 day")),
|
||||
(Timestamp("2018-01-01"), Timestamp("2018-01-02")),
|
||||
(
|
||||
Timestamp("2018-01-01", tz="US/Eastern"),
|
||||
Timestamp("2018-01-02", tz="US/Eastern"),
|
||||
),
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize("constructor", [IntervalArray, IntervalIndex])
|
||||
def test_is_empty(self, constructor, left, right, closed):
|
||||
# GH27219
|
||||
tuples = [(left, left), (left, right), np.nan]
|
||||
expected = np.array([closed != "both", False, False])
|
||||
result = constructor.from_tuples(tuples, closed=closed).is_empty
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
|
||||
class TestMethods:
|
||||
@pytest.mark.parametrize("new_closed", ["left", "right", "both", "neither"])
|
||||
def test_set_closed(self, closed, new_closed):
|
||||
# GH 21670
|
||||
array = IntervalArray.from_breaks(range(10), closed=closed)
|
||||
result = array.set_closed(new_closed)
|
||||
expected = IntervalArray.from_breaks(range(10), closed=new_closed)
|
||||
tm.assert_extension_array_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"other",
|
||||
[
|
||||
Interval(0, 1, closed="right"),
|
||||
IntervalArray.from_breaks([1, 2, 3, 4], closed="right"),
|
||||
],
|
||||
)
|
||||
def test_where_raises(self, other):
|
||||
# GH#45768 The IntervalArray methods raises; the Series method coerces
|
||||
ser = pd.Series(IntervalArray.from_breaks([1, 2, 3, 4], closed="left"))
|
||||
mask = np.array([True, False, True])
|
||||
match = "'value.closed' is 'right', expected 'left'."
|
||||
with pytest.raises(ValueError, match=match):
|
||||
ser.array._where(mask, other)
|
||||
|
||||
res = ser.where(mask, other=other)
|
||||
expected = ser.astype(object).where(mask, other)
|
||||
tm.assert_series_equal(res, expected)
|
||||
|
||||
def test_shift(self):
|
||||
# https://github.com/pandas-dev/pandas/issues/31495, GH#22428, GH#31502
|
||||
a = IntervalArray.from_breaks([1, 2, 3])
|
||||
result = a.shift()
|
||||
# int -> float
|
||||
expected = IntervalArray.from_tuples([(np.nan, np.nan), (1.0, 2.0)])
|
||||
tm.assert_interval_array_equal(result, expected)
|
||||
|
||||
msg = "can only insert Interval objects and NA into an IntervalArray"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
a.shift(1, fill_value=pd.NaT)
|
||||
|
||||
def test_shift_datetime(self):
|
||||
# GH#31502, GH#31504
|
||||
a = IntervalArray.from_breaks(date_range("2000", periods=4))
|
||||
result = a.shift(2)
|
||||
expected = a.take([-1, -1, 0], allow_fill=True)
|
||||
tm.assert_interval_array_equal(result, expected)
|
||||
|
||||
result = a.shift(-1)
|
||||
expected = a.take([1, 2, -1], allow_fill=True)
|
||||
tm.assert_interval_array_equal(result, expected)
|
||||
|
||||
msg = "can only insert Interval objects and NA into an IntervalArray"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
a.shift(1, fill_value=np.timedelta64("NaT", "ns"))
|
||||
|
||||
|
||||
class TestSetitem:
|
||||
def test_set_na(self, left_right_dtypes):
|
||||
left, right = left_right_dtypes
|
||||
left = left.copy(deep=True)
|
||||
right = right.copy(deep=True)
|
||||
result = IntervalArray.from_arrays(left, right)
|
||||
|
||||
if result.dtype.subtype.kind not in ["m", "M"]:
|
||||
msg = "'value' should be an interval type, got <.*NaTType'> instead."
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
result[0] = pd.NaT
|
||||
if result.dtype.subtype.kind in ["i", "u"]:
|
||||
msg = "Cannot set float NaN to integer-backed IntervalArray"
|
||||
# GH#45484 TypeError, not ValueError, matches what we get with
|
||||
# non-NA un-holdable value.
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
result[0] = np.nan
|
||||
return
|
||||
|
||||
result[0] = np.nan
|
||||
|
||||
expected_left = Index([left._na_value] + list(left[1:]))
|
||||
expected_right = Index([right._na_value] + list(right[1:]))
|
||||
expected = IntervalArray.from_arrays(expected_left, expected_right)
|
||||
|
||||
tm.assert_extension_array_equal(result, expected)
|
||||
|
||||
def test_setitem_mismatched_closed(self):
|
||||
arr = IntervalArray.from_breaks(range(4))
|
||||
orig = arr.copy()
|
||||
other = arr.set_closed("both")
|
||||
|
||||
msg = "'value.closed' is 'both', expected 'right'"
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
arr[0] = other[0]
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
arr[:1] = other[:1]
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
arr[:0] = other[:0]
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
arr[:] = other[::-1]
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
arr[:] = list(other[::-1])
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
arr[:] = other[::-1].astype(object)
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
arr[:] = other[::-1].astype("category")
|
||||
|
||||
# empty list should be no-op
|
||||
arr[:0] = []
|
||||
tm.assert_interval_array_equal(arr, orig)
|
||||
|
||||
|
||||
class TestReductions:
|
||||
def test_min_max_invalid_axis(self, left_right_dtypes):
|
||||
left, right = left_right_dtypes
|
||||
left = left.copy(deep=True)
|
||||
right = right.copy(deep=True)
|
||||
arr = IntervalArray.from_arrays(left, right)
|
||||
|
||||
msg = "`axis` must be fewer than the number of dimensions"
|
||||
for axis in [-2, 1]:
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
arr.min(axis=axis)
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
arr.max(axis=axis)
|
||||
|
||||
msg = "'>=' not supported between"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
arr.min(axis="foo")
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
arr.max(axis="foo")
|
||||
|
||||
def test_min_max(self, left_right_dtypes, index_or_series_or_array):
|
||||
# GH#44746
|
||||
left, right = left_right_dtypes
|
||||
left = left.copy(deep=True)
|
||||
right = right.copy(deep=True)
|
||||
arr = IntervalArray.from_arrays(left, right)
|
||||
|
||||
# The expected results below are only valid if monotonic
|
||||
assert left.is_monotonic_increasing
|
||||
assert Index(arr).is_monotonic_increasing
|
||||
|
||||
MIN = arr[0]
|
||||
MAX = arr[-1]
|
||||
|
||||
indexer = np.arange(len(arr))
|
||||
np.random.default_rng(2).shuffle(indexer)
|
||||
arr = arr.take(indexer)
|
||||
|
||||
arr_na = arr.insert(2, np.nan)
|
||||
|
||||
arr = index_or_series_or_array(arr)
|
||||
arr_na = index_or_series_or_array(arr_na)
|
||||
|
||||
for skipna in [True, False]:
|
||||
res = arr.min(skipna=skipna)
|
||||
assert res == MIN
|
||||
assert type(res) == type(MIN)
|
||||
|
||||
res = arr.max(skipna=skipna)
|
||||
assert res == MAX
|
||||
assert type(res) == type(MAX)
|
||||
|
||||
res = arr_na.min(skipna=False)
|
||||
assert np.isnan(res)
|
||||
res = arr_na.max(skipna=False)
|
||||
assert np.isnan(res)
|
||||
|
||||
res = arr_na.min(skipna=True)
|
||||
assert res == MIN
|
||||
assert type(res) == type(MIN)
|
||||
res = arr_na.max(skipna=True)
|
||||
assert res == MAX
|
||||
assert type(res) == type(MAX)
|
160
venv/lib/python3.12/site-packages/pandas/tests/arrays/interval/test_interval_pyarrow.py
Normal file
160
venv/lib/python3.12/site-packages/pandas/tests/arrays/interval/test_interval_pyarrow.py
Normal file
@ -0,0 +1,160 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import pandas as pd
|
||||
import pandas._testing as tm
|
||||
from pandas.core.arrays import IntervalArray
|
||||
|
||||
|
||||
def test_arrow_extension_type():
|
||||
pa = pytest.importorskip("pyarrow")
|
||||
|
||||
from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
|
||||
|
||||
p1 = ArrowIntervalType(pa.int64(), "left")
|
||||
p2 = ArrowIntervalType(pa.int64(), "left")
|
||||
p3 = ArrowIntervalType(pa.int64(), "right")
|
||||
|
||||
assert p1.closed == "left"
|
||||
assert p1 == p2
|
||||
assert p1 != p3
|
||||
assert hash(p1) == hash(p2)
|
||||
assert hash(p1) != hash(p3)
|
||||
|
||||
|
||||
def test_arrow_array():
|
||||
pa = pytest.importorskip("pyarrow")
|
||||
|
||||
from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
|
||||
|
||||
intervals = pd.interval_range(1, 5, freq=1).array
|
||||
|
||||
result = pa.array(intervals)
|
||||
assert isinstance(result.type, ArrowIntervalType)
|
||||
assert result.type.closed == intervals.closed
|
||||
assert result.type.subtype == pa.int64()
|
||||
assert result.storage.field("left").equals(pa.array([1, 2, 3, 4], type="int64"))
|
||||
assert result.storage.field("right").equals(pa.array([2, 3, 4, 5], type="int64"))
|
||||
|
||||
expected = pa.array([{"left": i, "right": i + 1} for i in range(1, 5)])
|
||||
assert result.storage.equals(expected)
|
||||
|
||||
# convert to its storage type
|
||||
result = pa.array(intervals, type=expected.type)
|
||||
assert result.equals(expected)
|
||||
|
||||
# unsupported conversions
|
||||
with pytest.raises(TypeError, match="Not supported to convert IntervalArray"):
|
||||
pa.array(intervals, type="float64")
|
||||
|
||||
with pytest.raises(TypeError, match="Not supported to convert IntervalArray"):
|
||||
pa.array(intervals, type=ArrowIntervalType(pa.float64(), "left"))
|
||||
|
||||
|
||||
def test_arrow_array_missing():
|
||||
pa = pytest.importorskip("pyarrow")
|
||||
|
||||
from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
|
||||
|
||||
arr = IntervalArray.from_breaks([0.0, 1.0, 2.0, 3.0])
|
||||
arr[1] = None
|
||||
|
||||
result = pa.array(arr)
|
||||
assert isinstance(result.type, ArrowIntervalType)
|
||||
assert result.type.closed == arr.closed
|
||||
assert result.type.subtype == pa.float64()
|
||||
|
||||
# fields have missing values (not NaN)
|
||||
left = pa.array([0.0, None, 2.0], type="float64")
|
||||
right = pa.array([1.0, None, 3.0], type="float64")
|
||||
assert result.storage.field("left").equals(left)
|
||||
assert result.storage.field("right").equals(right)
|
||||
|
||||
# structarray itself also has missing values on the array level
|
||||
vals = [
|
||||
{"left": 0.0, "right": 1.0},
|
||||
{"left": None, "right": None},
|
||||
{"left": 2.0, "right": 3.0},
|
||||
]
|
||||
expected = pa.StructArray.from_pandas(vals, mask=np.array([False, True, False]))
|
||||
assert result.storage.equals(expected)
|
||||
|
||||
|
||||
@pytest.mark.filterwarnings(
|
||||
"ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
|
||||
)
|
||||
@pytest.mark.parametrize(
|
||||
"breaks",
|
||||
[[0.0, 1.0, 2.0, 3.0], pd.date_range("2017", periods=4, freq="D")],
|
||||
ids=["float", "datetime64[ns]"],
|
||||
)
|
||||
def test_arrow_table_roundtrip(breaks):
|
||||
pa = pytest.importorskip("pyarrow")
|
||||
|
||||
from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
|
||||
|
||||
arr = IntervalArray.from_breaks(breaks)
|
||||
arr[1] = None
|
||||
df = pd.DataFrame({"a": arr})
|
||||
|
||||
table = pa.table(df)
|
||||
assert isinstance(table.field("a").type, ArrowIntervalType)
|
||||
result = table.to_pandas()
|
||||
assert isinstance(result["a"].dtype, pd.IntervalDtype)
|
||||
tm.assert_frame_equal(result, df)
|
||||
|
||||
table2 = pa.concat_tables([table, table])
|
||||
result = table2.to_pandas()
|
||||
expected = pd.concat([df, df], ignore_index=True)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
# GH#41040
|
||||
table = pa.table(
|
||||
[pa.chunked_array([], type=table.column(0).type)], schema=table.schema
|
||||
)
|
||||
result = table.to_pandas()
|
||||
tm.assert_frame_equal(result, expected[0:0])
|
||||
|
||||
|
||||
@pytest.mark.filterwarnings(
|
||||
"ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
|
||||
)
|
||||
@pytest.mark.parametrize(
|
||||
"breaks",
|
||||
[[0.0, 1.0, 2.0, 3.0], pd.date_range("2017", periods=4, freq="D")],
|
||||
ids=["float", "datetime64[ns]"],
|
||||
)
|
||||
def test_arrow_table_roundtrip_without_metadata(breaks):
|
||||
pa = pytest.importorskip("pyarrow")
|
||||
|
||||
arr = IntervalArray.from_breaks(breaks)
|
||||
arr[1] = None
|
||||
df = pd.DataFrame({"a": arr})
|
||||
|
||||
table = pa.table(df)
|
||||
# remove the metadata
|
||||
table = table.replace_schema_metadata()
|
||||
assert table.schema.metadata is None
|
||||
|
||||
result = table.to_pandas()
|
||||
assert isinstance(result["a"].dtype, pd.IntervalDtype)
|
||||
tm.assert_frame_equal(result, df)
|
||||
|
||||
|
||||
def test_from_arrow_from_raw_struct_array():
|
||||
# in case pyarrow lost the Interval extension type (eg on parquet roundtrip
|
||||
# with datetime64[ns] subtype, see GH-45881), still allow conversion
|
||||
# from arrow to IntervalArray
|
||||
pa = pytest.importorskip("pyarrow")
|
||||
|
||||
arr = pa.array([{"left": 0, "right": 1}, {"left": 1, "right": 2}])
|
||||
dtype = pd.IntervalDtype(np.dtype("int64"), closed="neither")
|
||||
|
||||
result = dtype.__from_arrow__(arr)
|
||||
expected = IntervalArray.from_breaks(
|
||||
np.array([0, 1, 2], dtype="int64"), closed="neither"
|
||||
)
|
||||
tm.assert_extension_array_equal(result, expected)
|
||||
|
||||
result = dtype.__from_arrow__(pa.chunked_array([arr]))
|
||||
tm.assert_extension_array_equal(result, expected)
|
@ -0,0 +1,93 @@
|
||||
"""Tests for Interval-Interval operations, such as overlaps, contains, etc."""
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from pandas import (
|
||||
Interval,
|
||||
IntervalIndex,
|
||||
Timedelta,
|
||||
Timestamp,
|
||||
)
|
||||
import pandas._testing as tm
|
||||
from pandas.core.arrays import IntervalArray
|
||||
|
||||
|
||||
@pytest.fixture(params=[IntervalArray, IntervalIndex])
|
||||
def constructor(request):
|
||||
"""
|
||||
Fixture for testing both interval container classes.
|
||||
"""
|
||||
return request.param
|
||||
|
||||
|
||||
@pytest.fixture(
|
||||
params=[
|
||||
(Timedelta("0 days"), Timedelta("1 day")),
|
||||
(Timestamp("2018-01-01"), Timedelta("1 day")),
|
||||
(0, 1),
|
||||
],
|
||||
ids=lambda x: type(x[0]).__name__,
|
||||
)
|
||||
def start_shift(request):
|
||||
"""
|
||||
Fixture for generating intervals of different types from a start value
|
||||
and a shift value that can be added to start to generate an endpoint.
|
||||
"""
|
||||
return request.param
|
||||
|
||||
|
||||
class TestOverlaps:
|
||||
def test_overlaps_interval(self, constructor, start_shift, closed, other_closed):
|
||||
start, shift = start_shift
|
||||
interval = Interval(start, start + 3 * shift, other_closed)
|
||||
|
||||
# intervals: identical, nested, spanning, partial, adjacent, disjoint
|
||||
tuples = [
|
||||
(start, start + 3 * shift),
|
||||
(start + shift, start + 2 * shift),
|
||||
(start - shift, start + 4 * shift),
|
||||
(start + 2 * shift, start + 4 * shift),
|
||||
(start + 3 * shift, start + 4 * shift),
|
||||
(start + 4 * shift, start + 5 * shift),
|
||||
]
|
||||
interval_container = constructor.from_tuples(tuples, closed)
|
||||
|
||||
adjacent = interval.closed_right and interval_container.closed_left
|
||||
expected = np.array([True, True, True, True, adjacent, False])
|
||||
result = interval_container.overlaps(interval)
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize("other_constructor", [IntervalArray, IntervalIndex])
|
||||
def test_overlaps_interval_container(self, constructor, other_constructor):
|
||||
# TODO: modify this test when implemented
|
||||
interval_container = constructor.from_breaks(range(5))
|
||||
other_container = other_constructor.from_breaks(range(5))
|
||||
with pytest.raises(NotImplementedError, match="^$"):
|
||||
interval_container.overlaps(other_container)
|
||||
|
||||
def test_overlaps_na(self, constructor, start_shift):
|
||||
"""NA values are marked as False"""
|
||||
start, shift = start_shift
|
||||
interval = Interval(start, start + shift)
|
||||
|
||||
tuples = [
|
||||
(start, start + shift),
|
||||
np.nan,
|
||||
(start + 2 * shift, start + 3 * shift),
|
||||
]
|
||||
interval_container = constructor.from_tuples(tuples)
|
||||
|
||||
expected = np.array([True, False, False])
|
||||
result = interval_container.overlaps(interval)
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"other",
|
||||
[10, True, "foo", Timedelta("1 day"), Timestamp("2018-01-01")],
|
||||
ids=lambda x: type(x).__name__,
|
||||
)
|
||||
def test_overlaps_invalid_type(self, constructor, other):
|
||||
interval_container = constructor.from_breaks(range(5))
|
||||
msg = f"`other` must be Interval-like, got {type(other).__name__}"
|
||||
with pytest.raises(TypeError, match=msg):
|
||||
interval_container.overlaps(other)
|
Reference in New Issue
Block a user